Pandas: Interpolate (upsample) non-equispaced timeseries into equispaced 18.0rc1

I want to interpolate (upscale) nonequispaced time-series to obtain equispaced time-series.

Currently I am doing it in following way:

1. take original timeseries.
2. create new timeseries with NaN values at each 30 seconds intervals ( using resample('30S').asfreq() )
3. concat original timeseries and new timeseries
4. sort the timeseries to restore order of times (This I do not like - sorting has complexity of O = n log(n) )
5. interpolate
6. remove original points from the timeseries

is there a more simple way? like in matlab you have original timeseries and you pass new times as a parameter to the interpolate() function to receive values at desired times. Ideally I would like to have a function such as

origTimeSeries.interpolate(newIndex=newTimeIndex, method='spline')

I remark that times of original timeseries might not be be a subset of the times of desired timeseries.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

values = [271238, 329285, 50, 260260, 263711]
timestamps = pd.to_datetime(['2015-01-04 08:29:4',
                             '2015-01-04 08:37:05',
                             '2015-01-04 08:41:07',
                             '2015-01-04 08:43:05',
                             '2015-01-04 08:49:05'])

ts = pd.Series(values, index=timestamps)
ts
ts[ts==-1] = np.nan
newFreq=ts.resample('60S').asfreq()

new=pd.concat([ts,newFreq]).sort_index()
new=new.interpolate(method='time')

ts.plot(marker='o')
new.plot(marker='+',markersize=15)

new[newFreq.index].plot(marker='.')

lines, labels = plt.gca().get_legend_handles_labels()
labels = ['original values (nonequispaced)', 'original + interpolated at new frequency (nonequispaced)', 'interpolated values without original values (equispaced!)']
plt.legend(lines, labels, loc='best')
plt.show()